Melanona represents an important cause of cancer-related mortality in the Western world, and its incidence has increased steadily over the past 3 decades. Advanced melanoma remains largely refractory to most therapeutic approaches; in part, this may reflect the considerable genetic complexity underlying melanoma and most other solid tumors. To this end, a main goal of cancer genomics is to characterize genetic alterations at a large scale within human tumors, in hopes of deriving molecular classification schema that might aid targeted therapeutic interdiction. High-density SNP microarrays, which simultaneously interrogate more than 100,000 SNP alleles, have recently been adapted for high-resolution cancer genome analysis. Prior work by the Sellers/Meyerson group and others showed that SNP arrays enabled detailed characterization of chromosomal gains, losses, and loss of heterozygosity (LOH) regions, even in the absence of matched normal samples. In addition, preliminary studies on the NCI60 panel of cancer cell lines demonstrated that combined analysis of SNP array and gene expression data, followed by functional validation, represented a powerful means of tumor classification and cancer gene identification. We are therefore applying an integrated, functional genomic approach to the study of metastatic melanoma. Specifically, high-density SNP array and comprehensive gene expression data will be collected on a large set of patient-derived, short-term melanoma cultures. Computational algorithms will then be employed to define and order the meaningful deletions, amplifications, and LOH regions across the sample set. Next, gene expression data will be combined with chromosomal information to identify candidate cancer genes within relevant genomic lesions. Finally, selected candidate oncogenes will be validated functionally by a lentiviral shRNA approach. These approaches should provide robust grounds for melanoma classification and gene discovery, thereby offering new options for future therapeutic interdiction.